A Numerical Investigation of Various Forms of Wavelet in Financial Time Series Analysis

被引：1

作者：

Sriwichai, Kittikorn ^{[1
]}

Sam-ang, Panu ^{[1
]}

Kaennakham, Sayan ^{[1
]}

机构：

[1] Suranaree Univ Technol, Sch Math, Nakhon Ratchasima, Thailand

来源：

INTERNATIONAL CONFERENCE ON ELECTRICAL, COMPUTER AND ENERGY TECHNOLOGIES (ICECET 2021) | 2021年

关键词：

wavelet transformation; mother wavelet; time series; financial signal processing; TRANSFORM;

D O I：

10.1109/ICECET52533.2021.9698629

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this work, seven forms of discrete wavelet transformation under the mother wavelet type are numerically studied for analyzing the financial time series data. They are Haar, Daubechies, Discrete FIR approximation of Meyer wavelet, Symlets, Coiflets, Biorthogonal, and Reverse biorthogonal. The data used consist of the Dow Jones Index (DJIA 30) from 17 July 2000 until 16 July 2020. Our numerical investigation shows that Haar performs best in solving the autocorrelation problem and denoising data.

引用

页码：2093 / 2098

页数：6

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